Breast cancer is the leading cause of death in women. Early detection, diagnosis and treatment of such cancer, however, can significantly improve the chances of survival of breast cancer patients. To date, x-ray mammography is the only proven diagnostic procedure capable of detecting early-stage, clinically occult breast cancers. Currently, the American Cancer Society recommends the use of mammography for asymptomatic screening of women over the age of 40, with annual mammography recommended for women over 50 years of age.
Cancer is detected indirectly utilizing mammography by identifying clusters of microcalcifications in the tissue of the diseased breast indicated by bright spots in the mammographic image. Indeed, subtle microcalcifications are typically the earliest, and sometimes the sole, radiographic indication of early breast cancer. Approximately half of all breast carcinomas exhibit such clusters of microcalcification in mammography images and up to 80% exhibit the clusters upon microscopic inspection. Thus, the early and accurate detection of such clusters is critical to diagnosis and early treatment of breast cancer patients.
Traditionally, mammography readings have been performed by radiologists using unaided visual inspection. The accuracy, inconsistency, speed and cost of such inspection, however, have limited the success rate of early detection utilizing this screening method. Additionally, the relatively small size and limited visibility of small, and thus early, microcalcifications prevent even the most experienced radiologists from detecting their presence. Moreover, with the education of women for the need for screening mammography, there has been an explosion in the number of mammograms performed each year. Accordingly, there have been efforts to develop automated screening of mammograms, such as computer-aided systems for detecting abnormal anatomical regions in digital medical images.
One such prior art method includes computation of filtered second spatial derivatives of intensity values of image pixels, preserving locations of zero-crossings in the filtered image, and evaluation of suspect conceptual feature measures. However, this method is particularly susceptible to errors caused by the high level of image noise accompanying most radiological images, resulting in a high number of false positives. Additionally, since microcalcifications are typically very small and irregularly shaped, a conceptual feature measure may not accurately and completely represent the actual nature of the microcalcification. Finally, the predetermined threshold values (i.e., a winner-take-all strategy) for each feature measure inherently limits the success of this method.
Thus a need remains for an automated method and system for digital imaging processing of radiologic images to detect the early presence of microcalcification clusters wherein the high spatial-frequency, small size and irregular shape of the clusters will be accurately and consistently detected.